{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "46f466ac-5382-4904-9f9f-fe281de63d3f",
   "metadata": {},
   "source": [
    "## Using Bayesian Methods to Optimize Reaction Conditions\n",
    "### Introduction \n",
    "\n",
    "In drug discovery, we frequently encounter situations where we need to modify a set of reaction conditions to optimize the yield. In an early discovery program, we may want to scale up a key intermediate to facilitate SAR exploration. In a later stage program, a process chemist may want to optimize a reaction for multi-kilo scale manufacture of the drug substance. A variety of strategies, ranging from intuition to design of experiments (DOE), are used to facilitate optimization. An alternative approach is to use Bayesian optimization, a technique that balances exploration and exploitation to identify optimal solutions. In this notebook, we will use [BayBE](https://github.com/emdgroup/baybe), an optimization library developed by scientists at Merck KGaA in Darmstadt, Germany.\n",
    "\n",
    "In this example, we will consider a reaction described in the supporting material of a 2020 paper by [Shields](https://www.nature.com/articles/s41586-021-03213-y) and coworkers from UCLA. \n",
    "\n",
    "       \n",
    "<center><img src=\"https://raw.githubusercontent.com/PatWalters/practical_cheminformatics_tutorials/main/reaction/images/bayes/Shields_reaction.png\" style=\"width: 50%; height: 50%\"/></center>\n",
    "\n",
    "We will optimize the 5 parameters shown in blue in the figure above.   Each of these parameters has a set of options available. \n",
    "\n",
    "<center><img src=\"https://raw.githubusercontent.com/PatWalters/practical_cheminformatics_tutorials/main/reaction/images/bayes/Possible_conditions.png\" style=\"width: 50%; height: 50%\"/></center>\n",
    "\n",
    "In total, there are 12 * 4 * 4 * 3 * 3 = 1728 possible combinations. We would like to identify the optimal conditions while running only a small number of experiments. Fortunately, Shields and coworkers investigated all 1728 combinations and provided a table with the conditions and corresponding yields. In this notebook, we will simulate a Bayesian optimization process. We will begin by identifying 10 initial reaction conditions. In practice, we would then run experiments to evaluate these conditions and record the corresponding reaction yields. However, in this case, we will look up the yields in a table. With the conditions and yields in hand, we can build a Bayesian model and use this model to select another 5 reaction conditions. We will then look up the yields for the 5 conditions and use this information to update the model. We will repeat this process through 5 rounds of optimization and examine the reaction yields for each optimization cycle. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cd298819-935f-4f84-827d-d19ea73e6cd8",
   "metadata": {},
   "source": [
    "### Overview \n",
    "\n",
    "\n",
    "Bayesian optimization with BayBE proceeds through the following steps. After this brief overview, we will execute the code to run each of these steps.\n",
    "\n",
    "1. **[Define the parameters](https://emdgroup.github.io/baybe/stable/userguide/parameters.html).** In this step, we define the reaction parameters to be optimized. Typically, we will optimize aspects such as the catalyst used in the reaction, the number of equivalents of catalyst, the reaction temperature, the reaction time, and the concentrations of reactants.\n",
    "2. **[Define a search space](https://emdgroup.github.io/baybe/stable/userguide/searchspace.html).** The search space defines the combination of parameters to be searched. The search space is typically defined using the function **[Searchspace.from_product](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.searchspace.core.SearchSpace.html#baybe.searchspace.core.SearchSpace.from_product)**, which creates a search space as the Cartesian product (all combinations) of the parameters. It's important not to confuse the Cartesian product of the parameters with the products of a reaction.\n",
    "3. **[Define the target](https://emdgroup.github.io/baybe/stable/userguide/targets.html).** The target is the quantity we are optimizing. In the case of reaction optimization, this is typically the yield. BayBE can optimize a single parameter or multiple parameters at once. In this notebook, we'll focus on single parameter optimization, where we are only optimizing the yield. The targets are combined into an objective, which configures how multiple targets are combined (if applicable).   \n",
    "4. **[Define a recommender](https://emdgroup.github.io/baybe/stable/userguide/recommenders.html).** The recommender selects the next set of experiments to be performed. In this case, we'll use BayBE's **[TwoPhaseMetaRecommender](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.recommenders.meta.sequential.TwoPhaseMetaRecommender.html#baybe.recommenders.meta.sequential.TwoPhaseMetaRecommender)**. This recommender behaves differently depending on whether it has experimental data. At the beginning of an optimization process, we typically don't have experimental data and want to find a diverse set of conditions to gather some initial data. If the **[TwoPhaseMetaRecommender](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.recommenders.meta.sequential.TwoPhaseMetaRecommender.html#baybe.recommenders.meta.sequential.TwoPhaseMetaRecommender)** has no data available, it uses **[Farthest Point Sampling](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.recommenders.pure.nonpredictive.sampling.FPSRecommender.html#baybe.recommenders.pure.nonpredictive.sampling.FPSRecommender)** to select a diverse set of initial conditions. If the recommender has data, it uses the **[BotorchRecommender](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.recommenders.pure.bayesian.botorch.BotorchRecommender.html#baybe.recommenders.pure.bayesian.botorch.BotorchRecommender)**, a Bayesian optimizer that balances exploration and exploitation when selecting sets of reaction conditions.\n",
    "5. **[Define a campaign](https://emdgroup.github.io/baybe/stable/userguide/campaigns.html).** In BayBE, the search space, objective, and recommender are combined into an object known as a **[Campaign](https://emdgroup.github.io/baybe/stable/userguide/campaigns.html)**. The Campaign has two important methods: **[recommend](https://emdgroup.github.io/baybe/stable/userguide/campaigns.html#getting-recommendations)**, which recommends the next set of experiments, and **[add_measurements](https://emdgroup.github.io/baybe/stable/userguide/campaigns.html#adding-measurements)**, which adds a set of experiments and updates the underlying Bayesian model. As mentioned above, if we call **recommend** before calling **add_measurements**, the **recommend** method uses farthest point sampling to select the next set of reaction conditions. If we call **recommend** after a call to **add_measurements**, the Bayesian model is used to select the next set of experiments.\n",
    "\n",
    "The figure below provides a schematic overview of the components described above. \n",
    "\n",
    "<center><img src=\"https://raw.githubusercontent.com/PatWalters/practical_cheminformatics_tutorials/main/reaction/images/bayes/baybe_architecture.png\"/></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06e06281-ca8f-4b95-9012-f95d71db9190",
   "metadata": {},
   "source": [
    "### Install and Load the Necessary Python Libraries\n",
    "On Google Colab this takes ~2.5 minutes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "55792731-b41a-4e9b-affd-e0dd4ae3bd1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import subprocess\n",
    " \n",
    "IN_COLAB = \"google.colab\" in sys.modules\n",
    " \n",
    "if IN_COLAB:\n",
    "    from IPython.utils import io\n",
    " \n",
    "    print(\"In Google Colab - installing required packages...\")\n",
    " \n",
    "    with io.capture_output():\n",
    "        subprocess.run(\n",
    "            [\"pip\", \"install\", 'baybe[chem,insights]', 'shap', 'tqdm', 'useful_rdkit_utils']\n",
    "        )\n",
    " \n",
    "    print(\"Done!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b98d2d89-c17a-40ac-8498-79d981b191ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "from baybe.targets import NumericalTarget\n",
    "from baybe.parameters import CategoricalParameter, NumericalDiscreteParameter, SubstanceParameter\n",
    "from baybe.searchspace import SearchSpace\n",
    "from baybe.objective import SingleTargetObjective\n",
    "from baybe.recommenders import BotorchRecommender, FPSRecommender, TwoPhaseMetaRecommender\n",
    "from baybe.campaign import Campaign\n",
    "\n",
    "from baybe.insights.shap import SHAPInsight\n",
    "from rdkit import Chem\n",
    "import useful_rdkit_utils as uru"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6eaa0b8f-ff62-4add-a0a1-cb434ef1bb4a",
   "metadata": {},
   "source": [
    "### 1. Define the parameters\n",
    "In this section, we will define two different parameter types. A **[CategoricalParameter](https://emdgroup.github.io/baybe/stable/userguide/parameters.html#categoricalparameter)** has a text label, we will encode these labels using a technique known as **[1-hot encoding](https://www.geeksforgeeks.org/ml-one-hot-encoding/)**, which is widely used in machine learning.  A **[NumericalDiscreteParameter](https://emdgroup.github.io/baybe/stable/userguide/parameters.html#numericaldiscreteparameter)** encodes numerical parameters, which adopt a specific set of possible values.  For instance, below we set temperature (Temp_C) to one of three different values 90,105, or 120. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9ea692f4-f82b-4b04-9888-1dea445ad74b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Base = {'KOAc': 'O=C([O-])C.[K+]',\n",
    " 'KOPv': 'O=C([O-])C(C)(C)C.[K+]',\n",
    " 'CsOAc': 'O=C([O-])C.[Cs+]',\n",
    " 'CsOPv': 'O=C([O-])C(C)(C)C.[Cs+]'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0579adae-332d-43b8-96f7-c5eaa5ad6489",
   "metadata": {},
   "outputs": [],
   "source": [
    "Ligand = {'BrettPhos': 'CC(C)C1=CC(C(C)C)=C(C(C(C)C)=C1)C2=C(P(C3CCCCC3)C4CCCCC4)C(OC)=CC=C2OC',\n",
    " 'PPht-Bu2': 'CC(C)(C)P(C1=CC=CC=C1)C(C)(C)C',\n",
    " 't-BuPh-CPhos': 'CN(C)C1=CC=CC(N(C)C)=C1C2=CC=CC=C2P(C(C)(C)C)C3=CC=CC=C3',\n",
    " 'PCy3': 'P(C1CCCCC1)(C2CCCCC2)C3CCCCC3',\n",
    " 'PPH3': 'P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3',\n",
    " 'XPhos': 'CC(C1=C(C2=CC=CC=C2P(C3CCCCC3)C4CCCCC4)C(C(C)C)=CC(C(C)C)=C1)C',\n",
    " 'P(fur)3': 'P(C1=CC=CO1)(C2=CC=CO2)C3=CC=CO3',\n",
    " 'PPh2Me': 'CP(C1=CC=CC=C1)C2=CC=CC=C2',\n",
    " 'GlorPhos': 'CC(OC1=C(P(C2CCCCC2)C3CCCCC3)C(OC(C)C)=CC=C1)C',\n",
    " 'JackiePhos': 'FC(F)(F)C1=CC(P(C2=C(C3=C(C(C)C)C=C(C(C)C)C=C3C(C)C)C(OC)=CC=C2OC)C4=CC(C(F)(F)F)=CC(C(F)(F)F)=C4)=CC(C(F)(F)F)=C1',\n",
    " 'CgMe-PPh': 'C[C@]1(O2)O[C@](C[C@]2(C)P3C4=CC=CC=C4)(C)O[C@]3(C)C1',\n",
    " 'PPhMe2': 'CP(C)C1=CC=CC=C1'}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d4d9e0c-8a05-4fbe-abef-737a2a602ca4",
   "metadata": {},
   "source": [
    "We can use the RDKit to draw the structures of the ligands.  The function **rd_make_structures_pretty** from the [useful_rdkit_utils](https://github.com/PatWalters/useful_rdkit_utils) library adjusts a few settings to make the RDKit structure drawings look better. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e4e4c499-9822-40eb-8ab9-beb57b3f2de6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uru.rd_make_structures_pretty()\n",
    "Chem.Draw.MolsToGridImage([Chem.MolFromSmiles(x) for x in Ligand.values()],molsPerRow=6, \n",
    "                          legends=Ligand.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8589ec00-33f9-46b2-a715-5da6eb16ceda",
   "metadata": {},
   "source": [
    "In the next few cells, we define the possible choices for solvent, concentration, and temperature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a507ac19-ef6b-4c90-83ae-8d7001ed43a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "Solvent = {'DMAc': 'CC(N(C)C)=O',\n",
    " 'BuCN': 'CCCC#N',\n",
    " 'BuOAc': 'CCCCOC(C)=O',\n",
    " 'p-Xylene': 'CC1=CC=C(C)C=C1'}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39f5264f-a1a8-46e2-9e85-bb2c1bb66072",
   "metadata": {},
   "source": [
    "The next two parameters are numeric we can simply define these as lists. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "551ad18e-2079-4373-b6c6-8e6f0ab8ddd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "Concentration = [0.057, 0.1, 0.153]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "45bda5bc-ea1b-4cab-b8e6-ff20b994799a",
   "metadata": {},
   "outputs": [],
   "source": [
    "Temp_C = [90.0, 105.0, 120.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0698278d-0d62-4691-a1dd-b42bb5c7062c",
   "metadata": {},
   "source": [
    "After parsing the input data, we use the corresponding values to define parameters.  As mentioned above, the first three are of type **[CategoricalParameter](https://emdgroup.github.io/baybe/stable/userguide/parameters.html#categoricalparameter)** and the next two are of type **[NumericalDiscreteParameter](https://emdgroup.github.io/baybe/stable/userguide/parameters.html#numericaldiscreteparameter)**. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4e49a4f9-b34c-495d-bcb8-784c1956bcef",
   "metadata": {},
   "outputs": [],
   "source": [
    "ohe_base_param = CategoricalParameter(values=Base.keys(), name=\"Base_Name\")\n",
    "ohe_ligand_param = CategoricalParameter(values=Ligand.keys(),name=\"Ligand_Name\")\n",
    "ohe_solvent_param = CategoricalParameter(values=Solvent.keys(),name=\"Solvent_Name\")\n",
    "ohe_concentration_param = NumericalDiscreteParameter(values=Concentration,name=\"Concentration\")\n",
    "ohe_temp_param = NumericalDiscreteParameter(values=Temp_C,name=\"Temp_C\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "212531cc-13ad-4ae1-a3da-09fb11f53c20",
   "metadata": {},
   "source": [
    "After defining each of the individual parameters, the parameters are combined into a list. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "29d6b5b8-6875-4259-8538-7313d950e989",
   "metadata": {},
   "outputs": [],
   "source": [
    "ohe_parameters = [ohe_base_param, ohe_ligand_param, ohe_solvent_param, ohe_concentration_param, ohe_temp_param]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "737c32dc-60ed-4d21-9035-b012d42ef3f4",
   "metadata": {},
   "source": [
    "### 2. Define the Searchspace\n",
    "This parameter list is then used to generate the **[Searchspace](https://emdgroup.github.io/baybe/stable/userguide/searchspace.html)**. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c4fd8e29-f1df-4f3c-b25c-b0d2857e7b18",
   "metadata": {},
   "outputs": [],
   "source": [
    "ohe_searchspace = SearchSpace.from_product(ohe_parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "749ccd78-1cdb-4204-a9e9-9196481419e3",
   "metadata": {},
   "source": [
    "Look at the size of the searchspace, which is 1728.  \n",
    "4 Base * 12 Ligand * 4 Solvent * 3 Concentration * 3 Temperature = 1728 conditions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1a5a097-3596-4495-bf8b-84e05f1d113e",
   "metadata": {},
   "source": [
    "BayBE has a utility method [estimate_product_space_size](https://emdgroup.github.io/baybe/stable/userguide/utils.html#search-space-memory-estimation) that can be used to estimate the size and memory requirements for the SearchSpace.  I like to use this as a quick check to ensure that I've defined the searchspace correctly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ffca0caa-c219-4149-8595-f75e87fd0582",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MemorySize(exp_rep_bytes=352512.0, exp_rep_shape=(1728, 5), comp_rep_bytes=304128, comp_rep_shape=(1728, 22))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe_searchspace.estimate_product_space_size(ohe_parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b191609e-6c4e-4bf1-bcf4-2b0f5c876c84",
   "metadata": {},
   "source": [
    "### 3. Define the Target\n",
    "In this case our **[Target](https://emdgroup.github.io/baybe/stable/userguide/targets.html)** is the yield.  We are only optimizing a single objective so we will use the BayBE **[SingleTargetOjective](https://emdgroup.github.io/baybe/stable/userguide/objectives.html#singletargetobjective)**. Note that we set **mode** to **MAX** indicating that we want to maximize the yield. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dc9355c5-b0d0-402b-8d91-2dd46a6ecbf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "ohe_target = NumericalTarget(name=\"yield\", mode=\"MAX\")\n",
    "ohe_objective = SingleTargetObjective(ohe_target)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9d5f283-a08c-46fc-b03b-39c78458d36d",
   "metadata": {},
   "source": [
    "### 4. Define the Recommender\n",
    "The [**Recommender**](https://emdgroup.github.io/baybe/stable/userguide/recommenders.html) selects the next set of experiments to try.  Note that we have configured **initial_recommender=FPSRecommender()**.  In this case, if there is no data available, the recommender uses **[farthest point sampling](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.recommenders.pure.nonpredictive.sampling.FPSRecommender.html#baybe.recommenders.pure.nonpredictive.sampling.FPSRecommender)** to select a diverse set of conditions.  If data is available, the Bayesian **[BotorchRecommender](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.recommenders.pure.bayesian.botorch.BotorchRecommender.html#baybe.recommenders.pure.bayesian.botorch.BotorchRecommender)** is used to balance exploration and exploitation and select the next set of reaction conditions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7440938b-bd91-4297-addc-d69cb77c764b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ohe_recommender = TwoPhaseMetaRecommender(\n",
    "    initial_recommender=FPSRecommender(),  # farthest point sampling\n",
    "    recommender=BotorchRecommender(),  # Bayesian model-based optimization\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "833d19da-4f0d-437d-a91f-a5a673a69358",
   "metadata": {},
   "source": [
    "### 5. Define the Campaign\n",
    "The Searchspace, Objective and Recommender are combined to produce a **[Campaign](https://emdgroup.github.io/baybe/stable/userguide/campaigns.html)**, which is used to suggest experiments and take in experimental data.  The Campaign has two primary methods: **[recommend](https://emdgroup.github.io/baybe/stable/userguide/campaigns.html#getting-recommendations)**, which provides suggested reaction conditions, and **[add_measurements](https://emdgroup.github.io/baybe/stable/userguide/campaigns.html#adding-measurements)**, which takes in experimental data and updates the Bayesian model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7f7e9eee-c3fb-4aea-8084-51d798c16a47",
   "metadata": {},
   "outputs": [],
   "source": [
    "ohe_campaign = Campaign(ohe_searchspace, ohe_objective, ohe_recommender)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c23309e6-b1d3-4c0d-bd08-9670ed53db5d",
   "metadata": {},
   "source": [
    "### 6. Get an Initial Set of Recommendations\n",
    "Now that the Campaign is defined, we can ask it for recommendations. So far, we haven't done any experiments. As such, the Campaign will use farthest point sampling to select a diverse set of initial conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "575ca441-2542-4d10-8b24-8991e3f7c884",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Base_Name</th>\n",
       "      <th>Ligand_Name</th>\n",
       "      <th>Solvent_Name</th>\n",
       "      <th>Concentration</th>\n",
       "      <th>Temp_C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CsOAc</td>\n",
       "      <td>BrettPhos</td>\n",
       "      <td>BuCN</td>\n",
       "      <td>0.057</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>CsOPv</td>\n",
       "      <td>CgMe-PPh</td>\n",
       "      <td>BuOAc</td>\n",
       "      <td>0.153</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>960</th>\n",
       "      <td>KOAc</td>\n",
       "      <td>GlorPhos</td>\n",
       "      <td>DMAc</td>\n",
       "      <td>0.153</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>KOPv</td>\n",
       "      <td>P(fur)3</td>\n",
       "      <td>p-Xylene</td>\n",
       "      <td>0.057</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1555</th>\n",
       "      <td>KOPv</td>\n",
       "      <td>PPh2Me</td>\n",
       "      <td>BuCN</td>\n",
       "      <td>0.153</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1090</th>\n",
       "      <td>KOAc</td>\n",
       "      <td>PPH3</td>\n",
       "      <td>BuOAc</td>\n",
       "      <td>0.057</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>CsOPv</td>\n",
       "      <td>JackiePhos</td>\n",
       "      <td>DMAc</td>\n",
       "      <td>0.057</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>CsOAc</td>\n",
       "      <td>PPhMe2</td>\n",
       "      <td>p-Xylene</td>\n",
       "      <td>0.153</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1632</th>\n",
       "      <td>KOPv</td>\n",
       "      <td>PPht-Bu2</td>\n",
       "      <td>BuOAc</td>\n",
       "      <td>0.100</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>CsOPv</td>\n",
       "      <td>XPhos</td>\n",
       "      <td>p-Xylene</td>\n",
       "      <td>0.100</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Base_Name Ligand_Name Solvent_Name  Concentration  Temp_C\n",
       "0        CsOAc   BrettPhos         BuCN          0.057    90.0\n",
       "485      CsOPv    CgMe-PPh        BuOAc          0.153   120.0\n",
       "960       KOAc    GlorPhos         DMAc          0.153    90.0\n",
       "1469      KOPv     P(fur)3     p-Xylene          0.057   120.0\n",
       "1555      KOPv      PPh2Me         BuCN          0.153   105.0\n",
       "1090      KOAc        PPH3        BuOAc          0.057   105.0\n",
       "559      CsOPv  JackiePhos         DMAc          0.057   105.0\n",
       "322      CsOAc      PPhMe2     p-Xylene          0.153   105.0\n",
       "1632      KOPv    PPht-Bu2        BuOAc          0.100    90.0\n",
       "822      CsOPv       XPhos     p-Xylene          0.100    90.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe_rec_1 = ohe_campaign.recommend(10)\n",
    "ohe_rec_1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a99faf5-398a-47e6-a419-c60f3b181fe9",
   "metadata": {},
   "source": [
    "### 7. Perform Experiments\n",
    "At this point we would typically perform a set of experiments using the 10 recommendations provided by the Campaign.  However, in the interest of efficiency, we will look up the reaction yields in a database.  To do this, we will define a Python class that reads a file with data from [Shields, et al](https://www.nature.com/articles/s41586-021-03213-yhttps://www.nature.com/articles/s41586-021-03213-y) and looks up the reaction yield.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "25dbab6c-51c0-4a00-bcfe-4f0cb7baab87",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ReactionLookup:\n",
    "    def __init__(self,infile_name):\n",
    "        self.df = pd.read_csv(infile_name)\n",
    "\n",
    "    def lookup_yields_from_Name(self, query):\n",
    "        query_cols = [\"Base_Name\",\"Ligand_Name\",\"Solvent_Name\",\"Concentration\",\"Temp_C\"]\n",
    "        return self.df.merge(query, on=query_cols)[query_cols + [\"yield\"]]\n",
    "\n",
    "reaction_lookup = ReactionLookup(\"https://raw.githubusercontent.com/PatWalters/practical_cheminformatics_tutorials/refs/heads/main/reaction/data/direct_arylation.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55c529a6-d475-4b91-84be-300f23f1d48a",
   "metadata": {},
   "source": [
    "Look up the yields for our first 10 conditions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "49959a5e-26ef-4910-affc-beda89617b24",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Base_Name</th>\n",
       "      <th>Ligand_Name</th>\n",
       "      <th>Solvent_Name</th>\n",
       "      <th>Concentration</th>\n",
       "      <th>Temp_C</th>\n",
       "      <th>yield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KOPv</td>\n",
       "      <td>PPht-Bu2</td>\n",
       "      <td>BuOAc</td>\n",
       "      <td>0.100</td>\n",
       "      <td>90</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CsOPv</td>\n",
       "      <td>XPhos</td>\n",
       "      <td>p-Xylene</td>\n",
       "      <td>0.100</td>\n",
       "      <td>90</td>\n",
       "      <td>36.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CsOAc</td>\n",
       "      <td>BrettPhos</td>\n",
       "      <td>BuCN</td>\n",
       "      <td>0.057</td>\n",
       "      <td>90</td>\n",
       "      <td>36.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KOAc</td>\n",
       "      <td>PPH3</td>\n",
       "      <td>BuOAc</td>\n",
       "      <td>0.057</td>\n",
       "      <td>105</td>\n",
       "      <td>1.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CsOPv</td>\n",
       "      <td>JackiePhos</td>\n",
       "      <td>DMAc</td>\n",
       "      <td>0.057</td>\n",
       "      <td>105</td>\n",
       "      <td>9.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KOPv</td>\n",
       "      <td>P(fur)3</td>\n",
       "      <td>p-Xylene</td>\n",
       "      <td>0.057</td>\n",
       "      <td>120</td>\n",
       "      <td>9.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>KOAc</td>\n",
       "      <td>GlorPhos</td>\n",
       "      <td>DMAc</td>\n",
       "      <td>0.153</td>\n",
       "      <td>90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>KOPv</td>\n",
       "      <td>PPh2Me</td>\n",
       "      <td>BuCN</td>\n",
       "      <td>0.153</td>\n",
       "      <td>105</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CsOAc</td>\n",
       "      <td>PPhMe2</td>\n",
       "      <td>p-Xylene</td>\n",
       "      <td>0.153</td>\n",
       "      <td>105</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CsOPv</td>\n",
       "      <td>CgMe-PPh</td>\n",
       "      <td>BuOAc</td>\n",
       "      <td>0.153</td>\n",
       "      <td>120</td>\n",
       "      <td>26.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Base_Name Ligand_Name Solvent_Name  Concentration  Temp_C  yield\n",
       "0      KOPv    PPht-Bu2        BuOAc          0.100      90   0.00\n",
       "1     CsOPv       XPhos     p-Xylene          0.100      90  36.98\n",
       "2     CsOAc   BrettPhos         BuCN          0.057      90  36.72\n",
       "3      KOAc        PPH3        BuOAc          0.057     105   1.94\n",
       "4     CsOPv  JackiePhos         DMAc          0.057     105   9.52\n",
       "5      KOPv     P(fur)3     p-Xylene          0.057     120   9.88\n",
       "6      KOAc    GlorPhos         DMAc          0.153      90   9.14\n",
       "7      KOPv      PPh2Me         BuCN          0.153     105   0.00\n",
       "8     CsOAc      PPhMe2     p-Xylene          0.153     105   0.00\n",
       "9     CsOPv    CgMe-PPh        BuOAc          0.153     120  26.69"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe_rec_1_results = reaction_lookup.lookup_yields_from_Name(ohe_rec_1)\n",
    "ohe_rec_1_results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3589aebe-a075-4ecc-882c-0ece3e69a5ed",
   "metadata": {},
   "source": [
    "Now that we've performed experiments, we need to add the data from the experiments to the Campaign.  We do this with the **add_measurements** method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "83d24412-bbfa-4c8a-923f-4967bab4ef74",
   "metadata": {},
   "outputs": [],
   "source": [
    "ohe_campaign.add_measurements(ohe_rec_1_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf35ece9-ad2a-4a77-8da7-ac4a2e7f01d2",
   "metadata": {},
   "source": [
    "Now let's simulate what we would do in practice. \n",
    "- Ask the Campaign for another set of 5 recommendations.  Now that we've added measurements, the Campaign uses the underlying Bayesian model to select the next set of reaction conditions.\n",
    "- Next we will look up the yield for that set of conditions and use the yield data to update the Bayesian model.\n",
    "\n",
    "We'll repeat this process 5 times and examine the distribution of yields at each iteration. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c81c918e-6271-4d32-9a50-4ac5e34e0d95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2a3425288bc140918108d614cae536ff",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in tqdm(range(0,5)):\n",
    "    rec = ohe_campaign.recommend(5)\n",
    "    rec_results = reaction_lookup.lookup_yields_from_Name(rec)\n",
    "    ohe_campaign.add_measurements(rec_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ba1e691-c2b9-4464-bfba-0c43f338cbbe",
   "metadata": {},
   "source": [
    "Next, let's compare the yield values over the six optimization cycles. We can ask the Campaign to provide a table with the measurements.  Note that the dataframe returned has a column **BatchNr** indicating the optimization cycle. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9061b0d0-93c9-4604-93df-cd557d4bfd67",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Base_Name</th>\n",
       "      <th>Ligand_Name</th>\n",
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       "      <th>FitNr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KOPv</td>\n",
       "      <td>PPht-Bu2</td>\n",
       "      <td>BuOAc</td>\n",
       "      <td>0.100</td>\n",
       "      <td>90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CsOPv</td>\n",
       "      <td>XPhos</td>\n",
       "      <td>p-Xylene</td>\n",
       "      <td>0.100</td>\n",
       "      <td>90</td>\n",
       "      <td>36.98</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CsOAc</td>\n",
       "      <td>BrettPhos</td>\n",
       "      <td>BuCN</td>\n",
       "      <td>0.057</td>\n",
       "      <td>90</td>\n",
       "      <td>36.72</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KOAc</td>\n",
       "      <td>PPH3</td>\n",
       "      <td>BuOAc</td>\n",
       "      <td>0.057</td>\n",
       "      <td>105</td>\n",
       "      <td>1.94</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CsOPv</td>\n",
       "      <td>JackiePhos</td>\n",
       "      <td>DMAc</td>\n",
       "      <td>0.057</td>\n",
       "      <td>105</td>\n",
       "      <td>9.52</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KOPv</td>\n",
       "      <td>P(fur)3</td>\n",
       "      <td>p-Xylene</td>\n",
       "      <td>0.057</td>\n",
       "      <td>120</td>\n",
       "      <td>9.88</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>KOAc</td>\n",
       "      <td>GlorPhos</td>\n",
       "      <td>DMAc</td>\n",
       "      <td>0.153</td>\n",
       "      <td>90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>KOPv</td>\n",
       "      <td>PPh2Me</td>\n",
       "      <td>BuCN</td>\n",
       "      <td>0.153</td>\n",
       "      <td>105</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CsOAc</td>\n",
       "      <td>PPhMe2</td>\n",
       "      <td>p-Xylene</td>\n",
       "      <td>0.153</td>\n",
       "      <td>105</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CsOPv</td>\n",
       "      <td>CgMe-PPh</td>\n",
       "      <td>BuOAc</td>\n",
       "      <td>0.153</td>\n",
       "      <td>120</td>\n",
       "      <td>26.69</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CsOPv</td>\n",
       "      <td>XPhos</td>\n",
       "      <td>p-Xylene</td>\n",
       "      <td>0.100</td>\n",
       "      <td>105</td>\n",
       "      <td>53.63</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CsOAc</td>\n",
       "      <td>BrettPhos</td>\n",
       "      <td>BuCN</td>\n",
       "      <td>0.100</td>\n",
       "      <td>90</td>\n",
       "      <td>48.21</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CsOPv</td>\n",
       "      <td>XPhos</td>\n",
       "      <td>p-Xylene</td>\n",
       "      <td>0.057</td>\n",
       "      <td>90</td>\n",
       "      <td>34.98</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CsOAc</td>\n",
       "      <td>BrettPhos</td>\n",
       "      <td>BuCN</td>\n",
       "      <td>0.057</td>\n",
       "      <td>105</td>\n",
       "      <td>45.11</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CsOPv</td>\n",
       "      <td>XPhos</td>\n",
       "      <td>p-Xylene</td>\n",
       "      <td>0.153</td>\n",
       "      <td>90</td>\n",
       "      <td>44.28</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CsOPv</td>\n",
       "      <td>XPhos</td>\n",
       "      <td>p-Xylene</td>\n",
       "      <td>0.057</td>\n",
       "      <td>120</td>\n",
       "      <td>39.32</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CsOAc</td>\n",
       "      <td>BrettPhos</td>\n",
       "      <td>BuCN</td>\n",
       "      <td>0.153</td>\n",
       "      <td>105</td>\n",
       "      <td>73.08</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CsOPv</td>\n",
       "      <td>XPhos</td>\n",
       "      <td>p-Xylene</td>\n",
       "      <td>0.153</td>\n",
       "      <td>105</td>\n",
       "      <td>73.93</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CsOAc</td>\n",
       "      <td>BrettPhos</td>\n",
       "      <td>BuCN</td>\n",
       "      <td>0.153</td>\n",
       "      <td>120</td>\n",
       "      <td>69.03</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CsOPv</td>\n",
       "      <td>XPhos</td>\n",
       "      <td>p-Xylene</td>\n",
       "      <td>0.153</td>\n",
       "      <td>120</td>\n",
       "      <td>68.31</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CsOAc</td>\n",
       "      <td>XPhos</td>\n",
       "      <td>BuCN</td>\n",
       "      <td>0.153</td>\n",
       "      <td>120</td>\n",
       "      <td>77.01</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CsOPv</td>\n",
       "      <td>BrettPhos</td>\n",
       "      <td>BuCN</td>\n",
       "      <td>0.153</td>\n",
       "      <td>120</td>\n",
       "      <td>53.81</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CsOAc</td>\n",
       "      <td>BrettPhos</td>\n",
       "      <td>BuOAc</td>\n",
       "      <td>0.153</td>\n",
       "      <td>120</td>\n",
       "      <td>20.60</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CsOPv</td>\n",
       "      <td>XPhos</td>\n",
       "      <td>BuOAc</td>\n",
       "      <td>0.153</td>\n",
       "      <td>120</td>\n",
       "      <td>59.05</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CsOPv</td>\n",
       "      <td>CgMe-PPh</td>\n",
       "      <td>p-Xylene</td>\n",
       "      <td>0.153</td>\n",
       "      <td>120</td>\n",
       "      <td>19.13</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CsOAc</td>\n",
       "      <td>XPhos</td>\n",
       "      <td>BuCN</td>\n",
       "      <td>0.100</td>\n",
       "      <td>120</td>\n",
       "      <td>84.17</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CsOAc</td>\n",
       "      <td>XPhos</td>\n",
       "      <td>BuCN</td>\n",
       "      <td>0.153</td>\n",
       "      <td>105</td>\n",
       "      <td>84.62</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CsOPv</td>\n",
       "      <td>XPhos</td>\n",
       "      <td>BuCN</td>\n",
       "      <td>0.153</td>\n",
       "      <td>105</td>\n",
       "      <td>79.76</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CsOPv</td>\n",
       "      <td>XPhos</td>\n",
       "      <td>BuCN</td>\n",
       "      <td>0.153</td>\n",
       "      <td>120</td>\n",
       "      <td>78.44</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>CsOAc</td>\n",
       "      <td>XPhos</td>\n",
       "      <td>p-Xylene</td>\n",
       "      <td>0.153</td>\n",
       "      <td>120</td>\n",
       "      <td>70.80</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>CsOAc</td>\n",
       "      <td>XPhos</td>\n",
       "      <td>BuCN</td>\n",
       "      <td>0.100</td>\n",
       "      <td>105</td>\n",
       "      <td>83.83</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>CsOPv</td>\n",
       "      <td>XPhos</td>\n",
       "      <td>BuCN</td>\n",
       "      <td>0.100</td>\n",
       "      <td>120</td>\n",
       "      <td>59.81</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>CsOAc</td>\n",
       "      <td>XPhos</td>\n",
       "      <td>BuCN</td>\n",
       "      <td>0.057</td>\n",
       "      <td>120</td>\n",
       "      <td>72.21</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>CsOAc</td>\n",
       "      <td>XPhos</td>\n",
       "      <td>BuCN</td>\n",
       "      <td>0.153</td>\n",
       "      <td>90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>CsOAc</td>\n",
       "      <td>XPhos</td>\n",
       "      <td>p-Xylene</td>\n",
       "      <td>0.153</td>\n",
       "      <td>105</td>\n",
       "      <td>76.63</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Base_Name Ligand_Name Solvent_Name  Concentration  Temp_C  yield  BatchNr  \\\n",
       "0       KOPv    PPht-Bu2        BuOAc          0.100      90   0.00        1   \n",
       "1      CsOPv       XPhos     p-Xylene          0.100      90  36.98        1   \n",
       "2      CsOAc   BrettPhos         BuCN          0.057      90  36.72        1   \n",
       "3       KOAc        PPH3        BuOAc          0.057     105   1.94        1   \n",
       "4      CsOPv  JackiePhos         DMAc          0.057     105   9.52        1   \n",
       "5       KOPv     P(fur)3     p-Xylene          0.057     120   9.88        1   \n",
       "6       KOAc    GlorPhos         DMAc          0.153      90   9.14        1   \n",
       "7       KOPv      PPh2Me         BuCN          0.153     105   0.00        1   \n",
       "8      CsOAc      PPhMe2     p-Xylene          0.153     105   0.00        1   \n",
       "9      CsOPv    CgMe-PPh        BuOAc          0.153     120  26.69        1   \n",
       "10     CsOPv       XPhos     p-Xylene          0.100     105  53.63        2   \n",
       "11     CsOAc   BrettPhos         BuCN          0.100      90  48.21        2   \n",
       "12     CsOPv       XPhos     p-Xylene          0.057      90  34.98        2   \n",
       "13     CsOAc   BrettPhos         BuCN          0.057     105  45.11        2   \n",
       "14     CsOPv       XPhos     p-Xylene          0.153      90  44.28        2   \n",
       "15     CsOPv       XPhos     p-Xylene          0.057     120  39.32        3   \n",
       "16     CsOAc   BrettPhos         BuCN          0.153     105  73.08        3   \n",
       "17     CsOPv       XPhos     p-Xylene          0.153     105  73.93        3   \n",
       "18     CsOAc   BrettPhos         BuCN          0.153     120  69.03        3   \n",
       "19     CsOPv       XPhos     p-Xylene          0.153     120  68.31        3   \n",
       "20     CsOAc       XPhos         BuCN          0.153     120  77.01        4   \n",
       "21     CsOPv   BrettPhos         BuCN          0.153     120  53.81        4   \n",
       "22     CsOAc   BrettPhos        BuOAc          0.153     120  20.60        4   \n",
       "23     CsOPv       XPhos        BuOAc          0.153     120  59.05        4   \n",
       "24     CsOPv    CgMe-PPh     p-Xylene          0.153     120  19.13        4   \n",
       "25     CsOAc       XPhos         BuCN          0.100     120  84.17        5   \n",
       "26     CsOAc       XPhos         BuCN          0.153     105  84.62        5   \n",
       "27     CsOPv       XPhos         BuCN          0.153     105  79.76        5   \n",
       "28     CsOPv       XPhos         BuCN          0.153     120  78.44        5   \n",
       "29     CsOAc       XPhos     p-Xylene          0.153     120  70.80        5   \n",
       "30     CsOAc       XPhos         BuCN          0.100     105  83.83        6   \n",
       "31     CsOPv       XPhos         BuCN          0.100     120  59.81        6   \n",
       "32     CsOAc       XPhos         BuCN          0.057     120  72.21        6   \n",
       "33     CsOAc       XPhos         BuCN          0.153      90   0.00        6   \n",
       "34     CsOAc       XPhos     p-Xylene          0.153     105  76.63        6   \n",
       "\n",
       "    FitNr  \n",
       "0     1.0  \n",
       "1     1.0  \n",
       "2     1.0  \n",
       "3     1.0  \n",
       "4     1.0  \n",
       "5     1.0  \n",
       "6     1.0  \n",
       "7     1.0  \n",
       "8     1.0  \n",
       "9     1.0  \n",
       "10    2.0  \n",
       "11    2.0  \n",
       "12    2.0  \n",
       "13    2.0  \n",
       "14    2.0  \n",
       "15    3.0  \n",
       "16    3.0  \n",
       "17    3.0  \n",
       "18    3.0  \n",
       "19    3.0  \n",
       "20    4.0  \n",
       "21    4.0  \n",
       "22    4.0  \n",
       "23    4.0  \n",
       "24    4.0  \n",
       "25    5.0  \n",
       "26    5.0  \n",
       "27    5.0  \n",
       "28    5.0  \n",
       "29    5.0  \n",
       "30    NaN  \n",
       "31    NaN  \n",
       "32    NaN  \n",
       "33    NaN  \n",
       "34    NaN  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe_measurement_df = ohe_campaign.measurements\n",
    "ohe_measurement_df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9f29b380-23c8-47f7-8576-f62f0e406368",
   "metadata": {},
   "source": [
    "To get an idea of how the reaction yields improve over multiple cycles, we can plot **yield** as a function of **batch number**.  Since the Bayesian Optimization routine balances exploration and exploitation, it can happen that later batches contain recommendations with lower yields that previous batches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1bc320c7-e0ca-470c-90df-0880b95a7368",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='BatchNr', ylabel='yield'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"BatchNr\",y=\"yield\",data=ohe_measurement_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eb2d22f-a458-4b22-8bbb-a07d19045806",
   "metadata": {},
   "source": [
    "### Optimizing With Substance Parameters \n",
    "In the sections above we used One-Hot Encoding to describe our reactants and catalysts. Each of the parameters was simply a label, the optimization process didn't include any notions of chemistry.  BayBE provides another parameter type called a [SubstanceParameter](https://emdgroup.github.io/baybe/stable/userguide/parameters.html#substanceparameter), where chemical entities are described by a set of molecular descriptors.   These descriptors enable the model to attribute similar behaviors to similar chemicals.  In the code block below, we define the base, ligand, and solvent with SubstanceParameters.  Note that this definition is similar to the way we defined the CategoricalParameters above, with two notable exceptions.  \n",
    "- First we define an encoding, which uses the **[Mordred](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0258-y)** descriptor calculator.\n",
    "- Second, we specify **decorrelate=0.7**, which removes correlated descriptors and makes the calculations more efficient.\n",
    "  \n",
    "Note that the concentration and temperature parameters are defined as above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6b6e4f22-6b34-4e53-9f5a-53b7bf763820",
   "metadata": {},
   "outputs": [],
   "source": [
    "substance_base_param = SubstanceParameter(data=Base, name=\"Base_Name\", encoding=\"MORDRED\", decorrelate=0.7)\n",
    "substance_ligand_param = SubstanceParameter(data=Ligand, name=\"Ligand_Name\", encoding=\"MORDRED\", decorrelate=0.7)\n",
    "substance_solvent_param = SubstanceParameter(data=Solvent, name=\"Solvent_Name\",encoding=\"MORDRED\",decorrelate=0.7)\n",
    "concentration_param = NumericalDiscreteParameter(values=Concentration,name=\"Concentration\")\n",
    "temp_param = NumericalDiscreteParameter(values=Temp_C,name=\"Temp_C\")\n",
    "substance_params = [substance_base_param, substance_ligand_param, substance_solvent_param, concentration_param, temp_param]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "007ab82f-010e-44cc-8222-3c8050151ce6",
   "metadata": {},
   "source": [
    "The Searchspace, Target, and Recommender are defined as above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3e4b6774-03b8-4a8b-a60a-43605fbcf2ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "substance_searchspace = SearchSpace.from_product(substance_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "edc85c95-de59-4889-930f-3d1a16396a8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "substance_target = NumericalTarget(name=\"yield\", mode=\"MAX\")\n",
    "substance_objective = SingleTargetObjective(substance_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a4a6a2ce-860b-4f6a-ade4-efe7745e68f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "substance_recommender = TwoPhaseMetaRecommender(\n",
    "    initial_recommender=FPSRecommender(),  # farthest point sampling\n",
    "    recommender=BotorchRecommender(),  # Bayesian model-based optimization\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8262e7a1-5807-41f1-abd3-e421c7bf2ac7",
   "metadata": {},
   "source": [
    "Combine the Searchspace, Objective, and Recommender to generate a Campaign. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8fd740bc-aa0b-49d5-bebf-57b9a71a2cec",
   "metadata": {},
   "outputs": [],
   "source": [
    "substance_campaign = Campaign(substance_searchspace, substance_objective, substance_recommender)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6818961d-60f6-439a-bdfe-9fbaaa85cf12",
   "metadata": {},
   "source": [
    "Get the initial recommendation using farthest point sampling. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "abe30b11-460a-4883-9bf0-921ec904beec",
   "metadata": {},
   "outputs": [],
   "source": [
    "substance_rec_1 = substance_campaign.recommend(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "df412516-41f9-45a9-93fd-60845b9cad8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Base_Name</th>\n",
       "      <th>Ligand_Name</th>\n",
       "      <th>Solvent_Name</th>\n",
       "      <th>Concentration</th>\n",
       "      <th>Temp_C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>CsOAc</td>\n",
       "      <td>JackiePhos</td>\n",
       "      <td>p-Xylene</td>\n",
       "      <td>0.057</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1493</th>\n",
       "      <td>KOPv</td>\n",
       "      <td>PCy3</td>\n",
       "      <td>BuOAc</td>\n",
       "      <td>0.153</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>CsOPv</td>\n",
       "      <td>P(fur)3</td>\n",
       "      <td>DMAc</td>\n",
       "      <td>0.057</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>CsOAc</td>\n",
       "      <td>PPhMe2</td>\n",
       "      <td>BuCN</td>\n",
       "      <td>0.057</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>935</th>\n",
       "      <td>KOAc</td>\n",
       "      <td>CgMe-PPh</td>\n",
       "      <td>p-Xylene</td>\n",
       "      <td>0.153</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>KOPv</td>\n",
       "      <td>t-BuPh-CPhos</td>\n",
       "      <td>BuOAc</td>\n",
       "      <td>0.057</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783</th>\n",
       "      <td>CsOPv</td>\n",
       "      <td>PPht-Bu2</td>\n",
       "      <td>p-Xylene</td>\n",
       "      <td>0.057</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CsOAc</td>\n",
       "      <td>BrettPhos</td>\n",
       "      <td>DMAc</td>\n",
       "      <td>0.153</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>KOAc</td>\n",
       "      <td>PPH3</td>\n",
       "      <td>p-Xylene</td>\n",
       "      <td>0.100</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1656</th>\n",
       "      <td>KOPv</td>\n",
       "      <td>XPhos</td>\n",
       "      <td>BuCN</td>\n",
       "      <td>0.057</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Base_Name   Ligand_Name Solvent_Name  Concentration  Temp_C\n",
       "135      CsOAc    JackiePhos     p-Xylene          0.057    90.0\n",
       "1493      KOPv          PCy3        BuOAc          0.153   120.0\n",
       "594      CsOPv       P(fur)3         DMAc          0.057    90.0\n",
       "288      CsOAc        PPhMe2         BuCN          0.057    90.0\n",
       "935       KOAc      CgMe-PPh     p-Xylene          0.153   120.0\n",
       "1703      KOPv  t-BuPh-CPhos        BuOAc          0.057   120.0\n",
       "783      CsOPv      PPht-Bu2     p-Xylene          0.057    90.0\n",
       "24       CsOAc     BrettPhos         DMAc          0.153    90.0\n",
       "1112      KOAc          PPH3     p-Xylene          0.100   120.0\n",
       "1656      KOPv         XPhos         BuCN          0.057    90.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "substance_rec_1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d779fad5-7a15-44fa-baa9-46548aa4f89e",
   "metadata": {},
   "source": [
    "Look up the yields for the initial 10 recommendations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "48d74b07-f8d7-4161-ba11-afd62cb7d43b",
   "metadata": {},
   "outputs": [],
   "source": [
    "substance_rec_1_results = reaction_lookup.lookup_yields_from_Name(substance_rec_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e2b4b5ad-7448-4312-a792-9edc9dcedf0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Base_Name</th>\n",
       "      <th>Ligand_Name</th>\n",
       "      <th>Solvent_Name</th>\n",
       "      <th>Concentration</th>\n",
       "      <th>Temp_C</th>\n",
       "      <th>yield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KOAc</td>\n",
       "      <td>PPH3</td>\n",
       "      <td>p-Xylene</td>\n",
       "      <td>0.100</td>\n",
       "      <td>120</td>\n",
       "      <td>5.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KOPv</td>\n",
       "      <td>XPhos</td>\n",
       "      <td>BuCN</td>\n",
       "      <td>0.057</td>\n",
       "      <td>90</td>\n",
       "      <td>30.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CsOPv</td>\n",
       "      <td>PPht-Bu2</td>\n",
       "      <td>p-Xylene</td>\n",
       "      <td>0.057</td>\n",
       "      <td>90</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KOPv</td>\n",
       "      <td>t-BuPh-CPhos</td>\n",
       "      <td>BuOAc</td>\n",
       "      <td>0.057</td>\n",
       "      <td>120</td>\n",
       "      <td>3.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CsOAc</td>\n",
       "      <td>BrettPhos</td>\n",
       "      <td>DMAc</td>\n",
       "      <td>0.153</td>\n",
       "      <td>90</td>\n",
       "      <td>6.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KOPv</td>\n",
       "      <td>PCy3</td>\n",
       "      <td>BuOAc</td>\n",
       "      <td>0.153</td>\n",
       "      <td>120</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CsOPv</td>\n",
       "      <td>P(fur)3</td>\n",
       "      <td>DMAc</td>\n",
       "      <td>0.057</td>\n",
       "      <td>90</td>\n",
       "      <td>21.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CsOAc</td>\n",
       "      <td>PPhMe2</td>\n",
       "      <td>BuCN</td>\n",
       "      <td>0.057</td>\n",
       "      <td>90</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CsOAc</td>\n",
       "      <td>JackiePhos</td>\n",
       "      <td>p-Xylene</td>\n",
       "      <td>0.057</td>\n",
       "      <td>90</td>\n",
       "      <td>5.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>KOAc</td>\n",
       "      <td>CgMe-PPh</td>\n",
       "      <td>p-Xylene</td>\n",
       "      <td>0.153</td>\n",
       "      <td>120</td>\n",
       "      <td>60.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Base_Name   Ligand_Name Solvent_Name  Concentration  Temp_C  yield\n",
       "0      KOAc          PPH3     p-Xylene          0.100     120   5.94\n",
       "1      KOPv         XPhos         BuCN          0.057      90  30.83\n",
       "2     CsOPv      PPht-Bu2     p-Xylene          0.057      90   0.00\n",
       "3      KOPv  t-BuPh-CPhos        BuOAc          0.057     120   3.88\n",
       "4     CsOAc     BrettPhos         DMAc          0.153      90   6.26\n",
       "5      KOPv          PCy3        BuOAc          0.153     120   0.00\n",
       "6     CsOPv       P(fur)3         DMAc          0.057      90  21.65\n",
       "7     CsOAc        PPhMe2         BuCN          0.057      90   0.00\n",
       "8     CsOAc    JackiePhos     p-Xylene          0.057      90   5.48\n",
       "9      KOAc      CgMe-PPh     p-Xylene          0.153     120  60.29"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "substance_rec_1_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "45394857-dbf2-4b4d-89e8-faac384de0ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "substance_campaign.add_measurements(substance_rec_1_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d4d2eaa-9ef1-4fc0-84b0-2ce964fd273f",
   "metadata": {},
   "source": [
    "Perform an additional 5 rounds of optimization, looking up experimental values for the reaction yields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4163b561-7966-4071-a5f9-333fcf953435",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cd6b1ffb0eb5447e82ead83f69a11e86",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in tqdm(range(0,5)):\n",
    "    substance_rec = substance_campaign.recommend(5)\n",
    "    substance_rec_results = reaction_lookup.lookup_yields_from_Name(substance_rec)\n",
    "    substance_campaign.add_measurements(substance_rec_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6fa71722-4d2b-4993-8344-78512a0a4fdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "substance_measurement_df = substance_campaign.measurements"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa2d6b0e-b9ec-4b5a-a0c5-0dfe70e5e481",
   "metadata": {},
   "source": [
    "To get an idea of how the reaction yields improve over multiple cycles, we can plot **yield** as a function of **batch number**.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "50e9aa5b-1fd7-45bb-a976-088f204015c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='BatchNr', ylabel='yield'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"BatchNr\",y=\"yield\",data=substance_measurement_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a5cd3c2-e391-4b4c-a218-ee223a448c59",
   "metadata": {},
   "source": [
    "### Assessing Feature Importance with BayBE Insights\n",
    "BayBE also has the ability to [assess the importance of features](https://emdgroup.github.io/baybe/stable/userguide/insights.html) to the model.  This capability uses the [SHAP](https://shap.readthedocs.io/en/latest/) library for interpreting machine learning models. A larger SHAP value indicates that a feature has a larger impact on the model predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "806efb6f-ca2d-4b08-801e-f850048875ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "insight = SHAPInsight.from_campaign(substance_campaign)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b729fb38-0bd0-4a0a-ab03-10082837cfe1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f172aaf0a05149a3864d8aa228d07f96",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/35 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "insight.plot(\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26b86ce2-19cb-4987-a3b4-1cd58500d43d",
   "metadata": {},
   "source": [
    "BayBE also supports a beeswarm plot where each point represents a set of reaction conditions.  Colors are used to represent the values of numeric parameters.  In the plot below, we see that we have higher SHAP values for higher temperatures and concentrations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d4dce41a-e5bf-4d7a-a803-fecea554b1f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "edd6a86d3c024b94a3ca0f439ecbaca9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/35 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "insight.plot(\"beeswarm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd9f8c6f-6ff5-4d0c-a67c-2fd153e2dc35",
   "metadata": {},
   "source": [
    "We can also pass a dataset as the second argument to **[insight.plot](https://emdgroup.github.io/baybe/stable/userguide/insights.html#plots)** and examine a single interation.  In the plot below, we only look at the first example. In this case we use a force plot, which uses the analogy of forces to illustrate how different features contribute to a model's output for a specific instance. \n",
    "- The **base value** is the mean value for all instances\n",
    "- **f(x)** shows the value for this instance\n",
    "- Values in red indicate parameters that increase the yield\n",
    "- Values in blue indicate parameters that decrease the yield\n",
    "- The size of each feature's contribution is indicated by the width of its corresponding section in the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b51d6c5f-0895-4a9f-b969-23a503736866",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "786d78deed294aa3922cce892b379ffb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "insight.plot(\"force\",explanation_index=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d185cbb4-9fe5-4def-9706-fefeb19bedd2",
   "metadata": {},
   "source": [
    "### Acknowledgements\n",
    "I'd like to thank Adrian Šošić, Martin Fitzner, and Alexander Hopp for releasing the BayBE package and for their valuable input on this tutorial.  I'd also to thank my colleague Seema Bag for motivating my initial interest in this topic. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e40b9000-bb0b-401c-92ce-a3b6095bd635",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
